{"id":"W1985300676","doi":"10.1371/journal.pone.0094308","title":"Sequential Decisions: A Computational Comparison of Observational and Reinforcement Accounts","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Institutes of Health Research; Heart and Stroke Foundation of Canada","keywords":"Reinforcement learning; Reinforcement; Artificial intelligence; Machine learning; Computer science; Cognitive psychology; Computational model; Observational study; Brain activity and meditation; Principle of maximum entropy; Sequence (biology); Entropy (arrow of time); Psychology; Neuroscience; Electroencephalography; Mathematics; Social psychology; Biology; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001713684,0.0003314379,0.0005931338,0.0005341274,0.0003758117,0.001371964,0.0018781,0.0007770594,0.004190471],"category_scores_gemma":[0.01257403,0.0003670937,0.0007105526,0.0004169503,0.00146846,0.002878901,0.0008029201,0.0009137553,0.0001567312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001185451,"about_ca_system_score_gemma":0.001030111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007154777,"about_ca_topic_score_gemma":0.004764002,"domain_scores_codex":[0.9995058,0.0002602689,0.00002355932,0.00007795017,0.0000815376,0.00005093469],"domain_scores_gemma":[0.9929964,0.005458975,0.000385866,0.0006179154,0.0002409353,0.0002999066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003545076,0.0001704093,0.008757507,0.0001135987,0.0001477954,0.0001961803,0.0006219589,0.7772897,0.001030957,0.1813196,0.0008314456,0.02916626],"study_design_scores_gemma":[0.00001419782,0.00001718491,0.0005932568,0.000004045852,0.000008033617,0.00001962172,0.00002487198,0.9504347,0.0001260467,0.04861744,0.0001339329,0.000006670527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6036212,0.0003376011,0.3813026,0.001867772,0.00007013368,0.00007876624,0.0003124081,0.0003977755,0.01201165],"genre_scores_gemma":[0.9852668,0.00008126059,0.0137157,0.00004017281,0.00001855045,0.00004117909,0.00008567642,0.00002674545,0.0007238686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007154777,"threshold_uncertainty_score":0.01422626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2167956337841406,"score_gpt":0.3206670283755635,"score_spread":0.1038713945914229,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}